


import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
 from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=1_3     ,   __snake_case=6_4     ,   __snake_case=2     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=3_2     ,   __snake_case=5     ,   __snake_case=4     ,   __snake_case=3_7     ,   __snake_case="gelu"     ,   __snake_case=0.1     ,   __snake_case=0.1     ,   __snake_case=1_0     ,   __snake_case=0.02     ,   __snake_case=[1, 1_6, 4, 4]     ,   __snake_case=None     ,   ):
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							is_training
         snake_case											=							use_labels
         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							type_sequence_label_size
         snake_case											=							initializer_range
         snake_case											=							scope
         snake_case											=							backbone_featmap_shape

         # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         # the number of patches is based on the feature map of the backbone, which by default uses an output stride
         # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
         snake_case											=							(self.image_size // 3_2) ** 2
         snake_case											=							num_patches + 1
    def 						a_     (			self       ):
         snake_case											=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         snake_case											=							None
         if self.use_labels:
              snake_case											=							ids_tensor([self.batch_size]     ,   self.type_sequence_label_size       )

         snake_case											=							self.get_config()

         return config, pixel_values, labels
    def 						a_     (			self       ):
         snake_case											=							{
             '''global_padding''': '''same''',
             '''layer_type''': '''bottleneck''',
             '''depths''': [3, 4, 9],
             '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
             '''embedding_dynamic_padding''': True,
             '''hidden_sizes''': [4, 8, 1_6, 3_2],
             '''num_groups''': 2,
         }

         return ViTHybridConfig(
             image_size=self.image_size     ,   patch_size=self.patch_size     ,   num_channels=self.num_channels     ,   hidden_size=self.hidden_size     ,   num_hidden_layers=self.num_hidden_layers     ,   num_attention_heads=self.num_attention_heads     ,   intermediate_size=self.intermediate_size     ,   hidden_act=self.hidden_act     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   is_decoder=__snake_case     ,   initializer_range=self.initializer_range     ,   backbone_featmap_shape=self.backbone_featmap_shape     ,   backbone_config=__snake_case     ,   )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							ViTHybridModel(config=__snake_case       )
         model.to(__snake_case       )
         model.eval()
         snake_case											=							model(__snake_case       )
         self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, self.seq_length, self.hidden_size)       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							self.type_sequence_label_size
         snake_case											=							ViTHybridForImageClassification(__snake_case       )
         model.to(__snake_case       )
         model.eval()
         snake_case											=							model(__snake_case     ,   labels=__snake_case       )
         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)       )




    def 						a_     (			self       ):
         snake_case											=							self.prepare_config_and_inputs()
         snake_case					,    snake_case					,    snake_case											=							config_and_inputs
         snake_case											=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class     A__   (       snake_case__			,   snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
    __magic_name__         =						(
        {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
        if is_torch_available()
        else {}
    )
    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    def 						a_     (			self       ):
         snake_case											=							ViTHybridModelTester(self       )
         snake_case											=							ConfigTester(self     ,   config_class=__snake_case     ,   has_text_modality=__snake_case     ,   hidden_size=3_7       )
    def 						a_     (			self       ):
         self.config_tester.run_common_tests()
    @unittest.skip(reason='''ViT does not use inputs_embeds'''       )
    def 						a_     (			self       ):
         pass
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              self.assertIsInstance(model.get_input_embeddings()     ,   (nn.Module)       )
              snake_case											=							model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(__snake_case     ,   nn.Linear       )       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              snake_case											=							inspect.signature(model.forward       )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              snake_case											=							[*signature.parameters.keys()]

              snake_case											=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__snake_case       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         snake_case											=							_config_zero_init(__snake_case       )
         for model_class in self.all_model_classes:
              snake_case											=							model_class(config=__snake_case       )
              # Skip the check for the backbone
              for name, module in model.named_modules():
                   if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                        snake_case											=							[F'''{name}.{key}''' for key in module.state_dict().keys()]
                        break

              for name, param in model.named_parameters():
                   if param.requires_grad:
                        if name in backbone_params:
                             continue
                        self.assertIn(
                            ((param.data.mean() * 1E9).round() / 1E9).item()     ,   [0.0, 1.0]     ,   msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''     ,   )




    @slow
    def 						a_     (			self       ):
         for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case											=							ViTHybridModel.from_pretrained(__snake_case       )
              self.assertIsNotNone(__snake_case       )






def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
     return image

@require_torch
@require_vision
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    @cached_property
    def 						a_     (			self       ):
         return (
             ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
             if is_vision_available()
             else None
         )
    @slow
    def 						a_     (			self       ):
         snake_case											=							ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(
             __snake_case       )

         snake_case											=							self.default_image_processor
         snake_case											=							prepare_img()
         snake_case											=							image_processor(images=__snake_case     ,   return_tensors='''pt'''       ).to(__snake_case       )

         # forward pass
         with torch.no_grad():
              snake_case											=							model(**__snake_case       )

         # verify the logits
         snake_case											=							torch.Size((1, 1_0_0_0)       )
         self.assertEqual(outputs.logits.shape     ,   __snake_case       )

         snake_case											=							torch.tensor([-1.9090, -0.4993, -0.2389]       ).to(__snake_case       )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,   __snake_case     ,   atol=1E-4       )       )




    @slow
    @require_accelerate
    def 						a_     (			self       ):
         snake_case											=							ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384'''       )
         snake_case											=							ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''     ,   device_map='''auto'''       )

         snake_case											=							prepare_img()

         snake_case											=							image_processor(images=__snake_case     ,   return_tensors='''pt'''       )
         snake_case											=							model(**__snake_case       )
         snake_case											=							outputs.logits
         # model predicts one of the 1000 ImageNet classes
         snake_case											=							logits.argmax(-1       ).item()

         self.assertTrue(model.config.idalabel[predicted_class_idx]     ,   '''tabby, tabby cat'''       )








class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case       ):
         snake_case											=							n
         snake_case											=							[None] * self.n
         snake_case											=							0  # index of the first element
         snake_case											=							0
         snake_case											=							0
    def __len__(			self       ):
         return self.size
    def 						a_     (			self       ):
         return self.size == 0
    def 						a_     (			self       ):
         return False if self.is_empty() else self.array[self.front]
    def 						a_     (			self     ,   __snake_case       ):
         if self.size >= self.n:
              raise Exception('''QUEUE IS FULL'''       )

         snake_case											=							data
         snake_case											=							(self.rear + 1) % self.n
         self.size += 1
         return self




    def 						a_     (			self       ):
         if self.size == 0:
              raise Exception('''UNDERFLOW'''       )

         snake_case											=							self.array[self.front]
         snake_case											=							None
         snake_case											=							(self.front + 1) % self.n
         self.size -= 1
         return temp




